近年來，義肢輔具的研究發展改善了截肢患者或運動功能障礙患者的生活機能，為了使義肢的使用更能符合人體運動學，也為了讓患者在使用義肢的過程中，可以藉由自主意念來操控義肢，新一代的義肢設計都著重於如何將生醫訊號應用於義肢功能的改良。本研究針對與肢體運動有直接關係的兩種生醫訊號，肌電訊號與神經電訊號，研究作為義肢控制源應用的可行性。首先，研究訊號本身的特質；其次，研發適當的訊號處理方法，從訊號中擷取出肢體運動有關的資訊，如關節出力與關節角度；再發展以生醫訊號為控制源之控制邏輯；最後，設計實驗，由實驗結果驗證控制法則的可行性。

　　在肌電訊號的研究方面，本文提出新的輔具控制方法，並在硬體上架構一肘關節扭矩輔助系統，利用受測者肱三頭肌與肱二頭肌的肌電訊號估測關節出力，並以之控制馬達施於肘關節的外加扭矩，扭矩大小等比例於受測者自主出力。由於肘關節兩側肌肉收縮的效率並不相同，而且肌電訊號與肌力之間為非線性關係，本研究利用關節固定在不同角度時，等長收縮產生不同程度的力矩來估測肌電圖訊號與關節力矩間的比值，作為肌電訊號控制馬達扭矩時的映射矩陣。此外，從運動生理學觀點來看，關節運動時，兩側肌肉的共同活化會增加關節運動的剛性，減低干擾的影響，使運動過程較為平穩，所以本研究在馬達控制訊號裡加入非線性阻尼，以模擬肌肉共同活化的效應，讓受測者可以在藉由馬達補償肌力的同時，能更穩定的控制整個人機系統。最後，將控制訊號經過適應濾波，在容許的範圍內適當的調整系統的頻寬，可使馬達輸出扭矩較為平順，增加系統的可適應性。實驗結果的統計分析證實，藉著以肌電訊號為控制源的輔具系統，中風患者可以較小的力量舉起重物，例如，僅以百分之五十的出力來舉起重物，並平穩的完成設定動作。

　　在神經電訊號的研究方面，本研究提出一個創新的神經性義肢設計，以量測到的神經電訊號迴授控制功能性神經電刺激系統，驅使肢體產生特定的運動，以期改善偏癱患者使用功能性電刺激的效能。相較於肌電訊號，神經電訊號的研究與應用發展較晚，其主要技術瓶頸在於神經電訊號很微弱，強度大小只有10 V，而且訊號裡包含很多與感覺運動有關生理訊息。本實驗室已研發出陣列式銬型電極可以量測到穩定清晰的神經電訊號，為了研究神經電訊號與肢體運動之間的相關性，本研究自行設計發展氣壓驅動測試平台，包含完成電路與迴路控制系統的架構及建立訊號擷取系統以量測神經電訊號、關節扭矩與角度。

　　本研究藉由動物實驗，已發展出三個神經電訊號處理的關鍵技術：首先為神經電訊號分離技術，可將神經內來自不同神經束的訊號予以分離，例如坐骨神經的神經電訊號包含來自遠端脛骨神經與腓骨神經的訊號，藉由在坐骨神經上不同方位量測到的兩組訊號，可以將脛骨神經與腓骨神經的訊號估測出來。其次為關節運動軌跡估測，利用所建立之數學模型，可以由神經電訊號估測出關節的運動軌跡。最後則是電刺激時的神經電訊號處理方法，電刺激會干擾神經電訊號的量測，經由本研究發展之訊號處理方法可以擷取出有生理意義的訊號，實驗證明經過處理的神經電訊號與電刺激所產生的關節扭矩有關，並建立其數學關係式。另外本研究也發展新式神經電刺激的控制模式，藉由上述這些神經電訊號處理技術與控制模式，本研究實現神經電刺激的迴路控制系統，並進行實驗以驗證神經電訊號做為控制源的可行性。

　Recently, the development of the prosthesis system has improved the daily life of amputees or patients with motor function diseases. For the claims of more functional and powerful prostheses, some innovative designs are proposed and emphasized the use of biomedical signal. The advantage of using the biomedical signal, especially for the prosthesis application, is that person can communicate with the machine system by the biomedical signal served as the man-machine interface. For example, amputees may control their prosthesis by controlling their residual muscle exertion. Therefore, the purpose of this study is to investigate the feasibility of using two kinds of biomedical signals, electromyogram (EMG) and electroneurogram (ENG), to be the control source of prosthesis. This study focuses on the methodology as to process the two biomedical signals, to extract the kinematical information, e.g. voluntary joint torque or joint angle during movement, from the signals and carry out the real-time control for prosthesis application.

　In this dissertation, an innovative control algorithm for prostheses has been developed by using the EMG signal. The triceps and biceps EMG signals are measured and used to estimate the elbow torque. The target of the EMG control algorithm is to control the external assistive torque proportional to the estimated elbow torque. To demonstrate the feasibility of the control algorithm, this study develops an assistive torque system which uses homogenic EMG signals to improve the elbow torque capability of stroke patients by applying an external assistive torque. To simplify the control algorithm, the ratios of the unilateral EMG signals to the elbow torque under isometric contraction at various elbow angles and torque levels are calculated and arranged as a mapping matrix. The applied assistive torque is proportional to the difference between the weighted EMG signals of the biceps and triceps determined by interpolating the mapping matrix. The overall stability of the assistive system is enhanced by the incorporation of a nonlinear damping element within the control algorithm which mimics the physiological damping of the elbow joint and the co-contraction between the biceps and triceps. Adaptive filtering of the control signal is also employed to achieve a balance between the bandwidth and the system adaptability so as to ensure a smooth assistive torque output.

　The results of tracking experiment demonstrate the ability of the assistive torque system to assist all the able-bodied subjects and stroke subjects in performing a number of tracking movements with reduced effort of agonist and with no sacrifice in elbow movement performance.

　About the study of ENG-based control algorithm, this study proposes an innovative design of neural prosthesis to implement autologous afferent sensing and electrical stimulation on nerves for ankle position control. The ENG signal has smaller amplitude, i.e. 10 uV, than the EMG signal. Technical difficulties involved in applying this approach to motor function restoration require developing techniques to extract useful, stable and repeatable signals and eliminating the artifacts induced by the electrical stimulation and nearby muscle activation. In this study, a multi-channel cuff electrode has developed and implanted around the peripheral nerve to measure the ENG signal. A computer-controlled pneumatic-driven dynamometer is designed to perform passive stretching of a rabbit’s ankle in order to minimize electrical disturbance from the control system under small ENG conditions.

　Three technologies for implementing the real-time control of neural prostheses have been developed, namely ENG signal separation, joint angle tracing and ENG signal processing on stimulated nerve. First, the ENG signals recorded with a multi-electrode cuff on the sciatic nerve are employed to investigate the possibility of extracting components ascending from the peroneal and tibial nerves. The results show that the signal separation model, which used only two channels of the sciatic recordings, is sufficient to separate the distal afferent components. Second, a simple empirical model is built based on the results of ENG measurement to estimate the peroneal and tibial nerve signals from the angular trajectory of ankle joint. After determining the parameters of the empirical model, an algorithm is proposed to estimated ankle joint angle trajectory by using the ENG signals of the peroneal and tibial nerves. Finally, an ENG signal processing is proposed to extract cleaner ENG signal measured from a stimulated nerve. These technologies provide a basis for the future investigation of biomimetic sensory feedback for functional neuromuscular stimulation (FNS) control of paralyzed limbs. On the other hand, an innovative control mode of electrical stimulation is proposed in this study. The hybrid amplitude/pulse-width modulation (AWM) of electrical stimulation performs better performance in ankle torque control than the traditional amplitude modulation. By the integration above-mentioned signal processing methods and the AWM control mode, a closed-loop control of FNS is realized in this study.

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